Infectious Disease

Classification of Antibacterial Peptides Using Long Short-Term Memory Recurrent Neural Networks.

Antimicrobial peptides are short amino acid sequences that may be antibacterial, antifungal, and antiviral. Most machine learning methodologies applied to identifying antibacterial peptides have developed feature vectors of identical lengths for each peptide in a given dataset although the peptides themselves may differ in number of amino acids. Features are often chosen which represent certain pe...

Antimicrobial activity of Dehn. plant extracts and essential oils: A review.

has become one of the world's most widely planted genera and (The River Red Gum) is a plantation species in many parts of the world. The plant traditional medical application indicates great antimicrobial properties, so essential oils and plant extracts have been widely examined. Essential oil of is active against many Gram positive (0.07-1.1%) and Gram negative bacteria (0.01-3.2%). The antib...

Modeling Antibacterial Activity with Machine Learning and Fusion of Chemical Structure Information with Microorganism Metabolic Networks.

Predicting the activity of new chemical compounds over pathogenic microorganisms with different metabolic reaction networks (MRN ) is an important goa...

Novel Machine Learning Identifies Brain Patterns Distinguishing Diagnostic Membership of Human Immunodeficiency Virus, Alcoholism, and Their Comorbidity of Individuals.

The incidence of alcohol use disorder (AUD) in human immunodeficiency virus (HIV) infection is twice that of the rest of the population. This study do...

Automatic diagnostics of tuberculosis using convolutional neural networks analysis of MODS digital images.

Tuberculosis is an infectious disease that causes ill health and death in millions of people each year worldwide. Timely diagnosis and treatment is ke...

Multiple Machine Learning Comparisons of HIV Cell-based and Reverse Transcriptase Data Sets.

The human immunodeficiency virus (HIV) causes over a million deaths every year and has a huge economic impact in many countries. The first class of dr...

Using artificial neural network and multivariate calibration methods for simultaneous spectrophotometric analysis of Emtricitabine and Tenofovir alafenamide fumarate in pharmaceutical formulation of HIV drug.

Spectrophotometric analysis method based on artificial neural network (ANN), partial least squares regression (PLS) and principal component regression...

A novel machine-learning-based infection screening system via 2013-2017 seasonal influenza patients' vital signs as training datasets.

Machine learning models for early sepsis recognition in the neonatal intensive care unit using readily available electronic health record data.

BACKGROUND: Rapid antibiotic administration is known to improve sepsis outcomes, however early diagnosis remains challenging due to complex presentati...

Unsupervised Learning Approach for Comparing Multiple Transposon Insertion Sequencing Studies.

Transposon insertion sequencing (TIS) is a widely used technique for conducting genome-scale forward genetic screens in bacteria. However, few methods...

AntiVPP 1.0: A portable tool for prediction of antiviral peptides.

Viruses are worldwide pathogens with a high impact on the human population. Despite the constant efforts to fight viral infections, there is a need to...

An attention based deep learning model of clinical events in the intensive care unit.

This study trained long short-term memory (LSTM) recurrent neural networks (RNNs) incorporating an attention mechanism to predict daily sepsis, myocar...

An effective and accurate identification system of Mycobacterium tuberculosis using convolution neural networks.

Tuberculosis (TB) remains the leading cause of morbidity and mortality from infectious disease in developing countries. The sputum smear microscopy re...

Severe Dengue Prognosis Using Human Genome Data and Machine Learning.

UNLABELLED: Dengue has become one of the most important worldwide arthropod-borne diseases. Dengue phenotypes are based on laboratorial and clinical e...

Adversarial training with cycle consistency for unsupervised super-resolution in endomicroscopy.

In recent years, endomicroscopy has become increasingly used for diagnostic purposes and interventional guidance. It can provide intraoperative aids f...

Application of recurrent neural network for online prediction of cell density of recombinant Pichia pastoris producing HBsAg.

Artificial neural networking (ANN) seems to be a promising soft sensor for implementing current approaches of quality by design (QbD) and process anal...

Hepatitis Screening and Treatment Campaign in Malaysia-Validation of Low-cost Point of Care Screening Tests and Nucleic Acid Tests for Hepatitis B and C.

BACKGROUND: Two major challenges in implementing budget-constrained Hepatitis screening and treatment campaign in Malaysia are the availability of low...

Argentinian multicenter study on urinary tract infections due to Streptococcus agalactiae in adult patients.

INTRODUCTION: Streptococcus agalactiae (group B streptococcus, GBS) is a recognized urinary pathogen both in males and pregnant or non-pregnant women....

Linezolid in Combination With Azoles Induced Synergistic Effects Against and Protected Against Experimental Candidiasis.

The incidence of resistant isolates has increased continuously in recent decades, especially . To overcome this resistance, research on antifungal se...

Using Machine Learning To Predict Antimicrobial MICs and Associated Genomic Features for Nontyphoidal .

Nontyphoidal species are the leading bacterial cause of foodborne disease in the United States. Whole-genome sequences and paired antimicrobial susce...